The Role of Non-State Actors In Sub-Saharan Africa
Initial Thoughts:
The immediate dilemma presented by the critical intelligence requirement (CIR): The Role of Non-State Actors in Sub-Saharan Africa, is the sheer scale of the question. It is unrealistic to develop in-depth, detailed analysis of every non-state actor (NSA) in the Sub-Saharan theater given time constraints. Therefore, it becomes the task of the analyst to develop an analytical methodology that can develop the most meaningful and indicative information, which can be acquired through a rather shallow analysis. Immediately the concept of the correlate presents the most promise to produce this analysis within operation confines. More specifically, information and context applied to the role of the NSA and the host country. These elements of contextual information must not only correlate to particular functions (roles) but also indicate NSA’s influence in geopolitics. Their must also be an understanding that elementism is dangerous; the context (environmental, social, ideological, etc.) must be clearly understood and weighted accordingly. These contextual correlates that possess predictive potential are best understood utilizing the term “indicator.” Indicators promise to produce the most predictive analysis with the shallowest level of collection.
First it will be necessary to operationally define the term role and non-state actor. Then data must be collected on targeted non-state actors and their host countries. Third level analysis should study the data to identify patterns and correlations.
Proposals for Conceptual Model Non-State Actors: 090507
Definitions:
State Actor: A person or organization that plays a role in politics and directly represents the governing power of a state and/or receives direct, obligatory direction from a state.
Non-State Actor: A person or organization which plays a role in politics and receives no direct, obligatory direction from a state.
Non-State Actor Model
Introduction (standard of measure):
Non-state actors have goals and undertake efforts to achieve those desired ends. Simple analysis of non-state actor activities and the historical result of the efforts can develop a picture of their effectiveness. The question of the role of a non-state actor should be broken down into two related perspectives. 1) The role could refer to a service or purpose it performs. 2) The role could refer to the influence it exerts on geopolitics beyond its simple purpose or service, whether intentional (directive) or not. A non-state actor’s influence will likely be seen in two dimensions. 1) A correlation between the non-state actor’s beliefs and philosophies and that of the culture in which they reside. 2) A correlation between the goals of non-state actors and the goals and ambitions of the culture in which they reside. These dimensions may materialize in popular support or simply in common cultural effort. Additionally other actors may act against the efforts of the non-state actor or subscribe to beliefs contrary to the non-state actor. The degree to which desired ends come from lateral cultural efforts or intrinsic target actor behaviors must be distinguished. Therefore, the success of a non-state actor should be considered separately yet related to the influence the non-state actor wields in its geopolitical environment.
Careful consideration should be paid to the service/purpose or functional role of the non-state actor. This is to say, what would exist in the absence of the non-state actor? And perhaps more importantly who knows it. If a non-state actor is performing a specific function other actors may leave the non-state actor alone or even secure its position. An example of a domestic non-state entity is a home owner’s association. Home owner’s associations carry out many civic functions (in some cases: trash collection, city code enforcement, etc) that the local government does not have to spend time and money to perform on its own. Home owners associations are often left alone, and even supported by local governments. Other actors may go to great lengths to keep non-state actors in place if they provide an important function. Another potential example of this is telecommunications is Somalia. While, warlords greedily grabbed up industry and wealth the telecommunication industry was generally left alone. Such a functional role may endow a non-state actor great influence or simply provide stability for an inefficacious cause or effort. Such functional roles are best glimpsed through the lens of network analysis. Network analysis should not only be conducted to determine the relationship between the non-state actor and other actors, but in addition it should be conducted to determine the relationship between the efforts and functional roles of non-state actors and the roles and efforts of other actors.
In order to determine the potential influence non-state actors may have on geopolitics in Africa and in turn US policy, analysis must take a multi-perspective approach. Collection should be guided identifying the key aspects of the non-state actor which are most indicative of the above mentioned characteristics. The role of a non-state actor is best defined by its movement toward desired ends minus lateral efforts (+/-) plus the popular support (+/-) it wields plus the functional role (+/-) it plays.
Non-State Actor category:
Non-State actors should be broken down into groups according to the nature of their efforts to acquire desired ends.
Multinational Corporations
Terrorist Groups
Islamic
Separatists
Paramilitary Groups
Tribal Orgs.
Sub-national Governments
Criminal Orgs
Drug producers/smugglers
Org Crime
Piracy
Diaspora
NGOs
Random Sampling:
The best way to gain an accurate intelligence picture of non-state actors in Africa is to identify populations and sub-populations (categorical non-state actors) and randomly select a sample to measure based on dimensions that are likely to indicate their role in geopolitics. The sample should be measured and compared for correlations between movement toward goal, functional role, and influence and the identified indicator dimensions. The dimensions should then be refined and organized according to the results. Theoretical causal relationships should be defined between the dimension and their indicated assessment. Once analysis is complete a second sample should be selected and assessed using the model to determine whether it accurately analyzes the second group.
Analytical Dimensions of Non-State Actors
Western-Individualist/ Non-Western-Collectivist
Hofstede’s (1980) cultural dimensions can be used to determine this value:
Individualism/Collectivism
Power Distance
Masculinity/Femininity
Uncertainty Avoidance
Significant data likely already exists for Africa
-Western-Individualism:
Western individualism generally emphasizes individual human rights, free market economy, and liberal democracies. Western culture generally exhibits more masculine qualities, has less power distance between superiors and subordinates, and has a high tolerance for risk or uncertainty.
-Non-Western-Collectivism:
Generally emphasizes family reputation, communal property, and service to the state or cause. Collectivist cultures generally exhibit more feminine qualities, has large power distance between superior and sub-ordinate and has a low tolerance for the unknown or uncertainty.
Match with culture:
This dimension refers to how well the ideology of the non-state actor matches the beliefs and ambitions of the culture in which they reside.
Connectedness:
This dimension refers to the number of other actor nodes the non-state actor node is connected to (network analysis). Furthermore non-state actor functional role nodes should be assessed to determine “role connectedness.” The nodes should be analyzed by: seniority, node fitness, node preference, and distance from hub.
Money:
Obviously non-state actors with more money can do more. The amount of money should be analyzed in terms of regional wealth; how much money is spent on efforts toward desired outcome vs. how much is held; and where the money comes from: revenue generating efforts (industry, crime, etc) vs. money is donated by populace.
Popular support/member:
This dimension measures both direct active membership (support) and indirect support. How many people hail to the cause and to what degree?
Operational Definitions: 090907
DMCIR (Critical Intelligence Requirement)
-Determine the role of non-state actors (NSA) in Sub-Saharan Africa.
-Study effectiveness of analytic methods; record and evaluate different methodologies
Questions from the DM
Process
-How do we do what we do?
-How do analysts use different methodologies and how do they improve our work?
Target
-What is the role of non-state actors in SSA (Sub-Saharan Africa)?
-Do we need to start dedicating resources to NSA?
-How do we characterize impact of NSA with that of state actors?
Definition: Non-State Actor
-A person or organization which plays a role in politics and receives minimal direct, obligatory direction from a state.
Terrorist Groups
Islamic
Separatists
Paramilitary Groups
Tribal Orgs.
Sub-national Governments
Criminal Orgs
Drug producers/smugglers
Org Crime
Piracy
Diaspora
NGOs
Definition: State-Actor
-A person or organization that plays a role in politics and directly represents the governing power of a state and/or receives direct, obligatory direction from a state.
Definition: Supra-State Actor
-A person or organization which plays a role in politics, normally consisting of multiple states, which exercises directive authority over multiple states.
· United Nations
· European Union
· African Union
Definition: Role
-A geopolitical actor's role in the international interplay of Sub-Saharan Africa have two perspectives from which assessments must be made:
1. Functional Role (the cog):
Does the non-state actor perform a function that will inspire other actors to preserve it?
Does the non-state actor perform a sub-function to a larger effort?
Does the non-state actor perform a function that relieves pressure on actors?
Does the non-state actor perform a function that empowers other actors? (religious leaders, etc)
Does the non-state actor perform a function that is paramount to stability in the region (to be defined)?
Region: Local, State, National, Supra National?
Is the non-state actor capable of maintaining its role in a fluid environment?
Dangers to non-state actor's role performance?
Indicators of change in non-state actor's role?
2. Influential Role in Geopolitics:
Does the non-state actor wield influence geopolitical interplay?
Does the non-state actor direct or control the behavior of other actors?
Does the non-state actor direct or control the behavior of a populace?
Does the non-state actor direct or control the perception of a populace?
Does a non-state actor perform a function which affords it the ability to negotiate, threaten, or pressure other actors into bending to its will?
Is the non-state actor exempt from certain local/state/international laws, mandates, or regulations?
Does the non-state actor gain undue funding from other actors?
Does the non-state actor directly oppose more powerful, more affluent actors.
Does the non-state actor represent the will and support of a larger populace?
Does the non-state actor maintain the negotiating authority between an ethnic, political, religious, etc populace and other actors?
Does the non-state actor command paramilitary, insurgent, partisan, etc. forces?
Does the non-state actor perform governance functions for a regional area and/or people?
Analytic Methods
Defined: any sort of technique that organizes information in order to create new information; strictly something that gives you an estimate (multipliers)
Final product:
· -process reports
· -study of analytic methods and their effectiveness
Notes on Conceptual Model (biases and confounds): 091107
Executive Summary
The Dilemma:
Most sources will take a particular stance or are produced from the view point of a particular interest. A review of articles, analyses, etc will likely not represent an objective standard of analysis. Other analysts are responding to the critical intelligence requirements (CIR) of a decision maker, or customer. Articles often serve the purposes of a particular interest, individual experience, or cater to sensational (recent and vivid) pieces of information. A review of such sources may produce some understanding of the issues and entities that individuals are paying attention to, yet it may fail to present a objective foundation of analysis.
Cause and Effect vs. Correlation:
Correlation is one of the most powerful tools of indication and prediction; however, correlation presents one of the significant confound dangers to analysis. Confounds are variables that vary systematically with the measured (target) aspects of analysis and often provide alternative explanation for cause and effect relationships. This is to say, two components of analysis may vary in a correlation to each other without having any causal relationship to each other. One potential explanation for this is the "third variable confound." To be sure, it is sometimes the case that two characteristics of analysis may react in a similar manner to an influence but do not react in accordance of the intrinsic nature of the other. Instead, both of the characteristics share a causal relationship to a third characteristic. The confound exists when one of the original characteristics exists in the absence of the "third characteristic." We then expect to see the second "correlated" characteristic, but it is not present in the absence of the third "causal" characteristic.
Assumptive Biases and Artifacts:
An analyst inevitably brings assumptions and heuristics to their analysis. Even a "common sense bias" may taint the analysis. This is to say, that some things are taken for granted because they make sense. We may conclude that the influence or efficacy of a separatist movement is based on its popular support. This is not always the case. A separatist movement may serve an important purpose for or represent political ideology more favorable to other more power actors in the region who fund and support the movement. Membership does not always equal popular support. Many separate movement in Africa raid villages and kidnap children to man their growing armies of separatists. Over structuring analysis may produce results consistent with the original hypothesis, yet fail to truly capture a broad generalizable analysis of the intelligence target. Such results are artifacts. If the contextual structure of an analysis is too specific it runs the danger of creating such artifacts. To be sure, narrowing specific criteria to analyze a broad question such as "the role of NSA in Sub-Saharan Africa" will produce analysis that is effective in only very specific circumstances, and will not serve broad analytical purposes.
The Atypical Correlate
In many historical studies it has been the case that the most reliable correlates have demonstrated little apparent connection to the hypothesis being studied. Whether by accident, imagination, or diligent study, researchers have uncovered lateral correlates that often represent the most indicative and reliable information in their analysis. Sometimes this correlate has been found by identifying "third variable confounds." These "diamond in the rough" patterns are sometimes difficult to find, but open-minded diligent analysis may uncover such intelligence gems.
The Algorithm and the Danger of Too Much Information:
Malcolm Gladwell, in his book Blink, describes how people sometimes use too much information to make a decision for a particular circumstance. Gladwell describes how the Cook County Hospital attempted to deal with the problem of predicting heart attacks in patients complaining of chest pain. A 4 factor algorithm, developed by Lee Goldman, proved significantly more effective than a doctor’s (expert’s) diagnosis (opinion/assessment). Gladwell’s conclusion is that we sometimes use too much information to produce our assessments of particular situations. We all know that family histories of heart attacks greatly increase one’s risk of having a heart attack in one’s life, but does it predict whether someone will have a heart attack over the next 72 hours? The answer is no, a family history simply presents an increased risk, but does not help identify the cause of chest pain, at least not significantly. Chest pain can be the result of several different causations (confounds), and an individual is likely to have many bouts of chest pain over their life, especially in later life. Genetic predisposition increases the chance of heart attacks primarily in later life. Therefore, genetic predisposition is a poor indicator for any 72 hour period. However, electrocardiographic (ECG) evidence, presence of fluid in the lungs, unstable angina, and systolic blood pressure in excess of 100mm Hg are good indicators of a looming heart attack threatening to strike in the next 72 hours. This means that any nurse or even you or I can be quickly trained to speedily predict a looming heart attack and outperform the average expert (cardiologist). The director of the Cook County Hospital, Dr. Brandon Reilly, found that the experts (doctors) were considering indicators such as family history, diabetes, cholesterol, etc which are long-term risk factors for heart attacks, but shed little light on the next 72 hours, and produce assessments with lower accuracy than those exclusively considering the 4 factor algorithm. The individual using the 4 factor algorithm is “high speed” going directly for the most effective indicators, and “low drag” ignoring information that is cumbersome and complicates the immediate need.
The Structured Analysis Approach:
Distilling the vast amount of information down into the most indicative and reliable pieces (indicators) is of paramount concern when tackling a query as broad as "determining the role of non-state actors in Sub-Saharan Africa." Once key indicative aspects of a non-state actor are identified, a general algorithmic model can be created for analyzing non-state actors. Anomaly-centric analysis can then be conducted to identify potential flaws in the algorithmic model. Such analysis will identify non-state actors that don't fit the model and can help refine the model. Using such a structured approach can combat analyst biases and prevent the danger of too much information.
3D Analysis: 09112007
3-D Analysis:
3-D analysis refers to the utilization of several dimensions (methods) of analysis to identify key indications of the query target. The analytical model demonstrates the interaction of multiple method analysis (see figure).
NSA-1 refers to non-state actor 1. NSA-1, in the above diagram, is analyzed on the basis of four indicators and four role assessment perspectives. NSA-1 analysis would consist of 16 cubes demonstrating the interaction between each indicator and each role assessment. Each cube would have an indicator value, a role assessment value and an interaction value (i.e. I-1 value A=R-1 value B...see diagram). Indicator interactions are then assessed and compared to reveal patterns in NSA indicators and NSA roles. These patterns can then be used to generate a indicator/role relationships. These relationships would then be the source of building a predictive model for the role of non-state actors in Sub-Saharan Africa.
3 Dimensional Analysis presents an opportunity to produce automated analysis. Least Squares has produced some advanced data systems capable of analyzing the data we collect. If our group was able to create a data source, a simple data mining machine would be capable of gathering and organizing data in multiple configurations. A genetically program analysis system could compare the configured data to weigh against defined values. For instance if we were to claim that indicator A correlates to characteristic value B then perfect correlation would result in 1 (A/B= 1). A genetically programmed data analysis system can make thousands of calculations to identify patterns and correlations. With the enhancement of fuzzy logic, the data analysis system could analyze the degree of correlation (i.e. A/B= 0.865 is a better (more successful) correlate than A/C= 0.658). Genetic programming paired with fuzzy logic could quickly identify informational elements that share the most correlation to analysis. Perhaps the data systems described in the Least Squares publication “Visualizing the Political Landscape” could be programmed for our purposes. I will consult with the group to see if they would consider approaching Mr. Reynolds and see if he has any programs that may serve our purposes.
Concept of Analysis: 091207
Conceptual Model of Analysis:
Analysis of the Role of Non-State Actors in Sub-Saharan Africa
Executive summary:
Team Least Squares is tasked with determining the role of non-state actors (NSA) in Sub-Saharan Africa. Additional guidance by the decision maker (Bill Reynolds) is to use multiple analytical techniques and maintain detailed documentation of these techniques for further assessment. Team Least Squares’ analytical concept is devised to utilize and compare the products of analytical techniques using a “3-Dimensional model.” This model will allow analysts and the decision maker to easily and efficiently see the interaction of multiple techniques and key indicators. The result will be a structured comparison of each technique and key indicator as they have been applied to a target non-state actor.
Initial Collection:
The initial collection will consist of a board survey of Sub-Saharan countries and the NSAs that reside within its borders. The survey will have three main purposes:
1) Initial collection will survey the available information and assess its meaning/relevance in international affairs.
2) Analysts will attempt to identify indicators of the role of NSAs in Sub-Saharan Africa.
3) Analysts will determine the analytical methods that will be most effective at answering the intelligence requirement
Indicators:
Some indicators will be obvious and typical. It is known that China’s strategy to acquire access to extractive industry is to gain access for the least cost, regardless of other political considerations. Therefore, countries in which extractive resources are owned by a minimal amount of individuals/interests present the greatest opportunity for China; where as, countries with privatized ownership of extractive resources would present China with multiple owners of oil field, mines, etc and inevitably cost more money to acquire such access. State ownership/ Private ownership of extractive resources is an indicator of China’s interest and engagement of a country for purposes of gaining access to extractive resources. What about atypical indicators?
Perhaps the percentage of a population afflicted with malaria or HIV directly correlates to membership in a terrorist or separatist organization. Perhaps the common religion or ideology of a country directly correlates to the strength of tribal bounds. Such indicators can only be identified through careful study. This is one of the purposes of the initial collection/ informational survey.
Analytical Methods:
Initial collection/informational survey will allow the analysts to develop an understanding of the “informational terrain” with regards to the role of NSA in Sub-Saharan Africa. Analysts will use this understanding of the informational terrain to select analytical methods most effective for developing analysis to satisfy the intelligence requirements. Analytical methods may include but are not limited to:
· Analysis of Competing Hypotheses
· Demographic Analysis
· Geographic Analysis
· Social Network Analysis
· Regression Analysis
· Link Analysis
· Citation Analysis
· Multi-Attribute Analysis
3-Dimensional Analysis:
3-Deminsional analysis refers to the use of multiple analytical techniques and their comparison to indicators. Team Least Squares will select several analytical methods to assessment the role of NSA in Sub-Saharan Africa. Those techniques will be used to assess individual NSAs. In order to compare a NSA to other actors, in terms of “role”, the analysts will devise a value scale to determine the functional and influential roles NSAs play in geopolitics. Analysts will also select the most accurate and indicative indicators, develop a similar value scale and assign values to each NSA. The analysts can then observe the interaction between analytical methods and indicators to identify patterns and the relationship between indicators and the assessments.
NSA-1 refers to non-state actor 1 (see figure above). NSA-1, in the above diagram, is analyzed on the basis of four indicators and four role assessment perspectives. NSA-1 analysis would consist of 16 cubes demonstrating the interaction between each indicator and each role assessment. Each cube would have an indicator value, a role assessment value and an interaction value (i.e. I-1 value “A”=R-1 value “B”...see diagram below). Indicator interactions are then assessed and compared to reveal patterns in NSA indicators and NSA roles. These patterns can then be used to generate indicator/role relationships. These relationships would then be the source of building a predictive model for the role of non-state actors in Sub-Saharan Africa.
3-Dimensional analysis also determines which analytical methods and indicators develop the most accurate intelligence picture of the different types of non-state actors (i.e. NGOs, paramilitary groups, etc). The end result is a measurable comparison of analytical method and indicator accuracy, as well as analytical confidence (see example 3-D matrix below).
Originally I proposed to the group that we structure our original collection efforts to collecting specific data pertinent to the issues surrounding non-state actors in sub-Saharan Africa. I even proposed a state-centric approach. This is to say identify characteristics of Sub-Saharan states that influence or encourage the behavior, existence, or success of non-state actors and indicate their role in geopolitics. This was rejected in favor of a unrestricted collection plan. This is reflected in the concept model as “survey of the informational terrain.” The group has decided to divide the countries in the Sub-Sahara among the members of the group. I have come to agree with the wisdom of group. While, a more structured method would produce more reliable conclusions, my plan runs the danger of following a scientific method where it would not serve the purposes of our analysis. This is to say, if we make a prediction (hypothesis) without an understanding of the informational terrain, we run the risk of only disproving our prediction and providing no real analysis. To be sure, if we start with a premise that certain indicators will demonstrate causal or correlative relationships to the target query (role of NSAs) we run the risk of concluding that indicators don’t demonstrate a causal or correlative relationship to the target query. This conclusion would not answer the CIR. This is not to say, that the idea is not dead, but we should have some feel for whether such relationships exist before pursuing the search for them.
I have decided to divide my unrestricted collection into three main parts: 1) Survey the works already produced on the role of non-state actors in Sub-Saharan Africa. 2) Produce a database of characteristics (mostly demographical data) of the countries assigned to me using the CIA World Factbook as a source. 3) List and categorize the NSAs in my countries, and attempt to develop to feel for their role and the information pertinent to.
3D Mod-Graphic 092307
The original 3D Analysis graphic was confusing so I created the following “Qubert” graphic to help the understanding of the concept.
The concept is truly simpler than it looks. Simply stated, by comparing each indicator with each method of analysis the analyst is able to identify patterns and correlations to determine the most efficient method of analysis. Furthermore, the design is conceptual in nature. It can be represented in more difficult for humans, but easier for computers to understand formats, such as Excel.
Correlation/Pattern analysis would be simple at this point. If high values of indicator one (I1), (such as % of population infected with HIV) are consistent with high value of “influential role” produced by all four analytical techniques (such as link analysis, financial analysis, citation analysis, and analysis of competing hypotheses) then I1 indicates that analysts in general will assess the target NSA to have a high value for “influential role.” Like wise if high values of role assessment 1 (R1) (such as link analysis), are consistent with multiple indicators (such, % of pop below poverty level, distribution of wealth, telecommunication assets etc) then it may be possible to develop an algorithm to predict the outcome of link analysis in terms of the target NSA. To be sure, poverty levels great than 30% matched with distribution of wealth lower than 10% and few telecommunications assets may indicate that tribal bounds are strong in the target country or NSA. Analysis across NSAs may develop reliable indicators and algorithms that produce effect analysis. Simple equations such as I1/R1= X can be used to produce comparable data. For instance: if I1= 30% and R1= 8 then I1/R1= .333/8=.0416. Values for multiple NSAs can be compared. If values for I1/R1 generally fall between .03 and .05 then the prediction can be made that I1 correlates to R1 regardless of the variation in the individual values for I1 and R1. Therefore if I1 for another NSA equaled 50% then we may predict that R1 would equal 12.019 (I1= .5 so .5/.0416= 12.019). Likewise if R1 for another NSA equaled 6 we may predict that I1 would equal 25% (R1= 6 so 6•.0416= .2496). Lets us say that, from a random sample, we developed a mean value of .0416 for I1/R1 with a standard deviation of .009 at a .05 significance level. We may then say if: NSA X’s I1= 25% then R1= 6 with a variation of R1= 4.94 to 7.669 (I1/.0416 +/- 1SD) within one standard deviation of the mean with a .05 error.
I believe such a method would answer both of the decision-maker’s CIRs: 1) Determine the role of non-state actors in Sub-Saharan Africa; 2) Use multiple analytical methods and keep notes (journals) for review after the assessment. Implied CIR: determine the efficacy and viability of different analytical methods. Furthermore, the scope of the CIR “the role of NSAs in Sub-Saharan Africa” is large and difficult to tackle. Therefore, by using a method that can identify indicators and test them for accuracy, the task becomes somewhat manageable.
Perhaps the best way to conceive the 3D analysis model is to consider it a multi-attribute/multiple target regression analysis. It not only allows the analyst to measure the interaction between different attributes (indicators) and analytical methods but also allows the analyst to determine the relationship between multiple targets and attribute/method interaction, ergo 3 dimensional analysis. Excel can easily be programmed to produce such data; furthermore we have access SPSS through the Mercyhurst Psychology Department. 3D analysis may seem daunting but given the scope of our CIR it is likely the most efficient process of fulfilling the requirement.
Notes: 092307
Issues:
Micro Credit (Small Loans):
These loans are used to help individuals “break the cycle of poverty” by purchasing land or entrepreneurial activities. Need to look into the affect of these. Reports seem optimistic about this type of credit; however, I believe these reports are likely skewed by the positive and stable regulatory environment which is the real source of success.
Notes on the 3D Model 092507
I spoke with Wheaton yesterday about the 3D model. He expressed a concern that it may be too exact for the data. His concern is that the available data may not be reliable enough, and too subjective for such a strenuous and exacting method. His point is well taken. Using statistical methods consistent with the standards of the scientific community would seemed to indicate that the data used is exact. This is not the case. As Wheaton pointed out, even the CIA World Factbook data is not precise and in many cases outdated. Moreover, the indicators are compared to analytical techniques; therefore, they would only indicate how an analyst would assess the query they are trying to answer and not necessarily reflect the reality of the situation.
We need to redesign the model to detect the “ghost in the machine.” A pattern is a pattern, is a pattern and must be explained. Moreover, patterns facilitate anomaly-centric analysis. Anomaly centric analysis often produces the meatiest analysis. This is to say, analysis of entities (NSAs) that don’t fit the pattern reveal the nuances of real-world dynamics. These nuances often temper analysis and allow the analyst to know when to weigh information differently, and penetrate the barrier of appearances. There are two main forces at play here. 1) The “Common Sense Bias” or the “that makes sense bias” (otherwise known as the O’Reilly Factor). 2) The anomaly and its indicators.
The “that makes sense bias” occurs when a conclusion appears to follow a logical course of reasoning but fails to account for the reality of the situation it is assessing, and goes unchallenged because of its apparently logical approach. This bias can take the familiar form of the “confirmation bias,” which occurs when the model precedes analysis. In this case, the analyst comes to a conclusion then builds a pseudo-logical explanation to confirm his/her original premise. This type of analysis is chronically prone to syllogistic errors, failure to recognize evidence to the contrary, the third variable confound, cultural-specific maxims (mirror imaging bias) etc. However, this bias can also take the form of a cognitive schema. These schemas present a more daunting task to recognize their effect. Generally, schemas are not dynamic, and do not react to a changing environment. The most dangerous schema (and most dangerous bias for that matter) of all is the paradigm. The paradigm is a complex universal schema that dictates the behaviors, excepted cause and effect relationships, thought processes, and so on of an entire population of people (normally of a profession, discipline, etc).
Anomalies and Indicators are signals and warnings that a model or paradigm does not fit the situation you are assessing. Albert Einstein launched a scientific revolution by identifying anomalies and indicators of the old paradigm of physics (Newtonian Physics). His work lead to new paradigms (quantum physics, etc) and still to this day scientists struggle to develop a new paradigm that can universally explain the known universe (unified theory). Anomaly-centric analysis allows the analyst to zero in on model spoilers, or confounds and often leads the analyst to the most important analytical questions and elements.
Applying these concepts to our current intelligence requirement we may conclude the following: An NGO that is dedicated to making the largest possible impact on the aids epidemic in Africa, will target the countries with the largest percentage of HIV infected population. However, this is unlikely the case. There is a plethora of geopolitical, security, and resource concerns that an NGO must deal with. If the original premise is not the case, why is it not the case? What are the common factors that are present in the countries where HIV focused NGOs concentrate their efforts. Likewise, we may conclude that: Countries, in which large percentages of their populations belong to many different tribes, will have a high degree of tribal conflicts. However, we may find that tribal in fighting correlates to the style of government or perhaps it correlates to the rate of HIV infection. If tribal in fighting correlates to the rate of HIV infection it is obvious that both correlate to a third unidentified variable, whether it be the supply of HIV medication, or even the supply of healthy fertile women, or simply the fact that the tribal balance of power has been offset by deaths due to aids. If we find that the tribal balance of power has been affected by HIV, we can, not only make assessments on the role of tribal organizations in the region but also determine the potential or actual role of an HIV focused NGO.
Anomaly-Centric Analysis 092507
Executive Summary:
Anomaly-Centric Analysis (ACA) is an abbreviated from of the scientific method of hypothesis testing. ACA begins by the analyst developing a simple pseudo-logical model of the expected influential factors of analysis. Next the analyst tests the model and searches for anomalies. The analyst targets anomalies and analyzes them to produce an advanced understanding of the cause and effect relationship between the influential factors of analysis. ACA is designed to identify the nuances and indicators that most provide insight into the reality of the situation or entity being assessed.
The Model:
Analysts generally cue into certain factors they believe to be at the heart of cause and effect relationships and or correlative elements (indicators) to produce predictive analysis. This often runs the risk of “confirmation bias.” Confirmation bias is present when a premise precedes analysis. Often the analyst will ignore evidence to the contrary of the premise, seek confirming evidence, utilized cultural-specific maxims (mirror-imaging bias), etc. However, the fact is that analysts cannot afford to take a “Humeian” approach and question all that they know. We as analysts must develop certain procedures and cognitive schemas to develop timely and accurate analysis. ACA is a structured analysis approach. To be sure, the analyst develops a model, collects specifically indicated information and uses standardized techniques to develop analysis in all situations.
ACA begins by the analyst making logical conclusions about the influential elements of his intelligence requirement (the query). The analyst then develops a model that he/she believes will develop accurate analysis. This process not only allows the analyst to develop structure for analysis, but it identifies the analyst’s assumptions about the intelligence requirement (IR). For example, an analyst may conclude that the more small tribes an African country has, the higher the rate of tribal conflict present in that country. Therefore, the analyst’s model would consist of the number of tribes, and the sizes of tribes, correlating to the rate of tribal conflict in the country. The analyst may create an equation as such:
[[image:file:C:%5CDOCUME%7E1%5CMARKBL%7E1%5CLOCALS%7E1%5CTemp%5Cmsohtml1%5C01%5Cclip_image013.gif width="564" height="180"]]
The analyst then begins to assess his model. Simply by producing a spreadsheet of the countries in the target region and their respective “# of tribes” and the “avg. percent of population belonging to these tribes” the analyst can then create a third column (inserting the equation Column A• Column B, or cell A1• B1) calculating the conflict coefficient. The analyst can then order the sheet by the conflict coefficient column and begin to test his/her model. At this point the analyst should develop method of determining the rate of conflict in the country. Using open source methods should provide the analyst ample information to determine how many armed conflicts, demonstrations, etc. a country has experienced for a specified time period. The analyst may simply list the numbers of conflict incidents or on going struggles that a country is experiencing or assign a scaled number value (e.g. 1-10). The analyst may start by comparing the countries containing the highest and lowest conflict coefficient. If the analyst’s model is accurate, there should be a relatively proportionate correlative variance between the conflict coefficient and actual rate of conflict between the countries. The analyst should proceed by comparing the entire list. If the analyst’s conflict coefficient and actual conflict rate does not correlate then he/she must look to other potential influential elements.
Perhaps the analyst determines that geographic location respective to the tribes is an influential factor in tribal conflict. The analyst may then add another variable to his/her equation. Perhaps the analyst determines that the average number of tribes bordering a single tribe’s territory is an influential factor. Perhaps he/she determines that the number of disputed territory is an influential factor, or for simplicity’s sake the analyst may determine that population density will suffice to address the issue. Therefore the analyst would augment the model and perhaps develop an equation as such:
Again the analyst tests the conflict coefficient to see if it generally correlates to the actual rate of conflict in the target countries. The analyst should continue to develop the model adding columns to his/her spreadsheet, combining and add factors in various ways until he/she develops model that generally reflects the reality of the intelligence query. If you can divide the list of countries into 3 or 4 groups (depending on the size of the sample) and can say that groups generally fall into an accurate lower, middle, and upper 33, or 25 percentiles, respectively, then you have likely created a model that reflects the major influential factors of your query. Building an exact model is not likely possible. The analyst should not attempt to account for every nuance of the query. When the model generally reflects reality, the analyst is ready for anomaly centric analysis.
Before we continue, the question should be addressed whether an analyst is capable of producing such a model. Analysts produce assessments based on the information available. They use indicative information to determine unknown factors, such as analysis of future events (predictive analysis), analysis of enemy behavior and strategy (counter-intuitive analysis), trends, patterns, tactics, techniques, etc. The very production of analysis insinuates that the analyst is able to survey available information determines its meaning/relevance, determine its reliability, and determine what the information indicates. Failure to create a model that generally represents the influential factors of an intelligence query should bring the analyst pause. Of course individual situations require individual consideration; however, analyst who are using schemas and weighing information in a way that does not reflect the major influences relevant to their intelligence query should reassess their analysis. We began by developing a model based on the analysts assumptions about the intelligence requirement, if the analyst fails to produce a model that generally reflects reality then the analyst should examine his/her assumptions. In this way ACA helps to battle analytical biases and address the issues raise by modern research into the psychology of intelligence analysis.
Anomaly Centric Analysis:
Once a suitable model has been developed to represent the majority of influential concerns of a target query, the analyst should begin to target the anomalies. While the model is far from perfect, due to the fact that you will likely never develop a model that can respond to dynamism of the real world, the model does serve the purpose of identifying the entities that do not fit the model. This is where the analyst finds the nuances of individual situations. Any analyst will tell you that you must address each dimension of context (environmental, social, political, etc), specific to the intelligence requirement in order to create reliable analysis, anomaly centric analysis is where the analysts addresses these contextual elements. If a target-specific context exerts its influence it is likely to distort the target queries position in the model. For instance, perhaps a country does not fit the model. Country X has a few large tribes, which have historically secure borders. The majority of control in the government is determined by resources which reside well within the boundaries of tribal territory. The tribes have, for decades, welded relatively equivalent power and influence in the country. Conflicts have been restricted to a few border regions, and have not generally affected the government’s stability. However, over the past few years inter-tribal fighting has significantly increased and increasing deeper border incursion have been reported by one tribe. Assessments have increasingly indicated that stability in the region is deteriorating, and civil war seems all but a matter of time. You know that the country does not fit the model. You know that there is a factor beyond tribe number, size, and population density. You identify an anomaly and you target it for analysis. It does not matter what you find, perhaps an HIV epidemic has offset the balance of power between the tribes, perhaps there is a new Islamic or communist movement moving through the country. Your conclusions however, become an indicator. You may conclude that a regional rise in HIV is an indicator that tribal conflict will increase. The nuances identified in anomaly centric analysis can develop into a system of indicators and warnings, indicating trends and likely events. They may indicate the affects of an up coming election. An HIV epidemic may start in one country and grow. The identified nuance/indicator may grow to become a primary factor in the model. ACA can evolve over time to react to a dynamic environment. Any analyst knows that analysis is never done; it grows and changes with the times. Analysts can share, tweak, and discuss models and spreadsheets. Over time models can be perfected, and adapt to fit a changing world. ACA clearly presents the analyst’s assumptions for other analysts to evaluate, it is an interactive, competitive, and naturally select (based on success) process.
Conclusion:
Although the above example is purely theoretical, it demonstrates the adaptive, analytical nature of ACA. ACA develops and tests the analyst’s assumptions, identifies the influential anatomy of an intelligence query, helps the analyst target nuances, and identifies indicators and warnings. ACA is a process built to evolve. Someday you may use the Blair Model of Tribal Conflict, and later the Blair-Smith Model, which competes with the Johnson-Rodriquez Model. The information age has brought analysts closer than ever, using ACA thousands of models may compete for dominance, judged by their accuracy and flexibility. Information systems coded with fuzzy logic, and genetic programming may someday produce automated intelligence, identifying anomalies and bringing them to the attention of the analyst. ACA’s structured approach provides some common ground from which assessments may be analyzed and compared to other assessments. ACA develops a platform from which analysis is converted from ambiguous processes to clearly understandable process, freeing up the analyst to concentrate on the nuanced nature of intelligence queries.
Success!
We have done it! In a meeting with Wheaton today we were describing our new environmental, state-centric approach. In this approach the assumption is that role precedes the actor. The team came up with a new approach to measure how socio-political environments enhance and inhibit the roles of non-state actors (NSA). We talked about using an unfolding scale. A score of 0 would indicate total government control and no role for NSAs. From this point the role of NSA can move in two directions as the state governments demonstrate less control over the socio-political environment. First, a government can willing relinquish control over the activities of NSA, as well as guarantee and regulate them. This would include forms of popular representation, lobbyism, unionization, freedoms afforded by constitutions, the purchase and sale of mineral rights, privatization of economy etc. This type of willing relinquishment of power provides the potential for NSA to play and maintain influential roles. However, this dynamic would enhance the roles of lawful actors, such as labor unions, lawful corporations, activists, NGOs, etc. In contrast these types of regulated freedoms, or liberties and the institutions created to guarantee and enforce them would inhibit the role of unlawful actors such as insurgent groups, criminal organizations, corrupt corporations, etc. The second way a government can lose power is by its inability to regulate the behavior of NSAs. Unlawful NSAs use power sources that are beyond the regulation and sanction of the government. These “extra-governmental” power sources/behaviors include violence, intimidation, bribery, extortion, etc. Therefore, by our model, two states with the score of 4 in either direction of loss of government control have equivalent roles in terms of influence for NSAs, except in one state the role is filled by lawful actors, such as the corporations and political groups of the US, and in the other state the role is filled by unlawful actors, such as the Warlords of Somalia.
Wheaton upon hearing this drew a picture on the white board. He explained that what we had done, was to develop a spectrum by which we can represent NSA in terms of state control and represent the two different types of actors. An hour of conversation later, developing Wheaton’s drawing the model was born. We were able to identify the extremes and the middle of such a spectrum. On one extreme we have libertarian states, dead center we should find totalitarian states, and on the other extreme we should find anarchic states. Therefore, any score or ranking on an index or country data consistent with totalitarian states should represent the center of the model or a 0. We can now represent all types of environments which enhance the different types of NSAs. The group is now all on the same page. From now on I suspect my journal entries will be in our methodology descriptions.
Analysis Journal: Mark Blair
The Role of Non-State Actors In Sub-Saharan Africa
Initial Thoughts:
The immediate dilemma presented by the critical intelligence requirement (CIR): The Role of Non-State Actors in Sub-Saharan Africa, is the sheer scale of the question. It is unrealistic to develop in-depth, detailed analysis of every non-state actor (NSA) in the Sub-Saharan theater given time constraints. Therefore, it becomes the task of the analyst to develop an analytical methodology that can develop the most meaningful and indicative information, which can be acquired through a rather shallow analysis. Immediately the concept of the correlate presents the most promise to produce this analysis within operation confines. More specifically, information and context applied to the role of the NSA and the host country. These elements of contextual information must not only correlate to particular functions (roles) but also indicate NSA’s influence in geopolitics. Their must also be an understanding that elementism is dangerous; the context (environmental, social, ideological, etc.) must be clearly understood and weighted accordingly. These contextual correlates that possess predictive potential are best understood utilizing the term “indicator.” Indicators promise to produce the most predictive analysis with the shallowest level of collection.
First it will be necessary to operationally define the term role and non-state actor. Then data must be collected on targeted non-state actors and their host countries. Third level analysis should study the data to identify patterns and correlations.
Proposals for Conceptual Model Non-State Actors: 090507
Definitions:
State Actor: A person or organization that plays a role in politics and directly represents the governing power of a state and/or receives direct, obligatory direction from a state.
Non-State Actor: A person or organization which plays a role in politics and receives no direct, obligatory direction from a state.
Non-State Actor Model
Introduction (standard of measure):
Non-state actors have goals and undertake efforts to achieve those desired ends. Simple analysis of non-state actor activities and the historical result of the efforts can develop a picture of their effectiveness. The question of the role of a non-state actor should be broken down into two related perspectives. 1) The role could refer to a service or purpose it performs. 2) The role could refer to the influence it exerts on geopolitics beyond its simple purpose or service, whether intentional (directive) or not. A non-state actor’s influence will likely be seen in two dimensions. 1) A correlation between the non-state actor’s beliefs and philosophies and that of the culture in which they reside. 2) A correlation between the goals of non-state actors and the goals and ambitions of the culture in which they reside. These dimensions may materialize in popular support or simply in common cultural effort. Additionally other actors may act against the efforts of the non-state actor or subscribe to beliefs contrary to the non-state actor. The degree to which desired ends come from lateral cultural efforts or intrinsic target actor behaviors must be distinguished. Therefore, the success of a non-state actor should be considered separately yet related to the influence the non-state actor wields in its geopolitical environment.
Careful consideration should be paid to the service/purpose or functional role of the non-state actor. This is to say, what would exist in the absence of the non-state actor? And perhaps more importantly who knows it. If a non-state actor is performing a specific function other actors may leave the non-state actor alone or even secure its position. An example of a domestic non-state entity is a home owner’s association. Home owner’s associations carry out many civic functions (in some cases: trash collection, city code enforcement, etc) that the local government does not have to spend time and money to perform on its own. Home owners associations are often left alone, and even supported by local governments. Other actors may go to great lengths to keep non-state actors in place if they provide an important function. Another potential example of this is telecommunications is Somalia. While, warlords greedily grabbed up industry and wealth the telecommunication industry was generally left alone. Such a functional role may endow a non-state actor great influence or simply provide stability for an inefficacious cause or effort. Such functional roles are best glimpsed through the lens of network analysis. Network analysis should not only be conducted to determine the relationship between the non-state actor and other actors, but in addition it should be conducted to determine the relationship between the efforts and functional roles of non-state actors and the roles and efforts of other actors.
In order to determine the potential influence non-state actors may have on geopolitics in Africa and in turn US policy, analysis must take a multi-perspective approach. Collection should be guided identifying the key aspects of the non-state actor which are most indicative of the above mentioned characteristics. The role of a non-state actor is best defined by its movement toward desired ends minus lateral efforts (+/-) plus the popular support (+/-) it wields plus the functional role (+/-) it plays.
Non-State Actor category:
Non-State actors should be broken down into groups according to the nature of their efforts to acquire desired ends.
Random Sampling:
The best way to gain an accurate intelligence picture of non-state actors in Africa is to identify populations and sub-populations (categorical non-state actors) and randomly select a sample to measure based on dimensions that are likely to indicate their role in geopolitics. The sample should be measured and compared for correlations between movement toward goal, functional role, and influence and the identified indicator dimensions. The dimensions should then be refined and organized according to the results. Theoretical causal relationships should be defined between the dimension and their indicated assessment. Once analysis is complete a second sample should be selected and assessed using the model to determine whether it accurately analyzes the second group.
Analytical Dimensions of Non-State Actors
Western-Individualist/ Non-Western-Collectivist
-Western-Individualism:
Western individualism generally emphasizes individual human rights, free market economy, and liberal democracies. Western culture generally exhibits more masculine qualities, has less power distance between superiors and subordinates, and has a high tolerance for risk or uncertainty.
-Non-Western-Collectivism:
Generally emphasizes family reputation, communal property, and service to the state or cause. Collectivist cultures generally exhibit more feminine qualities, has large power distance between superior and sub-ordinate and has a low tolerance for the unknown or uncertainty.
Match with culture:
This dimension refers to how well the ideology of the non-state actor matches the beliefs and ambitions of the culture in which they reside.
Connectedness:
This dimension refers to the number of other actor nodes the non-state actor node is connected to (network analysis). Furthermore non-state actor functional role nodes should be assessed to determine “role connectedness.” The nodes should be analyzed by: seniority, node fitness, node preference, and distance from hub.
Money:
Obviously non-state actors with more money can do more. The amount of money should be analyzed in terms of regional wealth; how much money is spent on efforts toward desired outcome vs. how much is held; and where the money comes from: revenue generating efforts (industry, crime, etc) vs. money is donated by populace.
Popular support/member:
This dimension measures both direct active membership (support) and indirect support. How many people hail to the cause and to what degree?
Operational Definitions: 090907
DMCIR (Critical Intelligence Requirement)
-Determine the role of non-state actors (NSA) in Sub-Saharan Africa.
-Study effectiveness of analytic methods; record and evaluate different methodologies
Questions from the DM
Process
-How do we do what we do?
-How do analysts use different methodologies and how do they improve our work?
Target
-What is the role of non-state actors in SSA (Sub-Saharan Africa)?
-Do we need to start dedicating resources to NSA?
-How do we characterize impact of NSA with that of state actors?
Definition: Non-State Actor
-A person or organization which plays a role in politics and receives minimal direct, obligatory direction from a state.
Definition: State-Actor
-A person or organization that plays a role in politics and directly represents the governing power of a state and/or receives direct, obligatory direction from a state.
Definition: Supra-State Actor
-A person or organization which plays a role in politics, normally consisting of multiple states, which exercises directive authority over multiple states.
· United Nations
· European Union
· African Union
Definition: Role
-A geopolitical actor's role in the international interplay of Sub-Saharan Africa have two perspectives from which assessments must be made:
1. Functional Role (the cog):
- Does the non-state actor perform a function that is paramount to stability in the region (to be defined)?
- Region: Local, State, National, Supra National?
- Is the non-state actor capable of maintaining its role in a fluid environment?
- Dangers to non-state actor's role performance?
- Indicators of change in non-state actor's role?
2. Influential Role in Geopolitics:Analytic Methods
Defined: any sort of technique that organizes information in order to create new information; strictly something that gives you an estimate (multipliers)
Types:
· -Regression
· -social network
· -ACH
· -link charts
Final product:
· -process reports
· -study of analytic methods and their effectiveness
Notes on Conceptual Model (biases and confounds): 091107
Executive Summary
The Dilemma:
Most sources will take a particular stance or are produced from the view point of a particular interest. A review of articles, analyses, etc will likely not represent an objective standard of analysis. Other analysts are responding to the critical intelligence requirements (CIR) of a decision maker, or customer. Articles often serve the purposes of a particular interest, individual experience, or cater to sensational (recent and vivid) pieces of information. A review of such sources may produce some understanding of the issues and entities that individuals are paying attention to, yet it may fail to present a objective foundation of analysis.
Cause and Effect vs. Correlation:
Correlation is one of the most powerful tools of indication and prediction; however, correlation presents one of the significant confound dangers to analysis. Confounds are variables that vary systematically with the measured (target) aspects of analysis and often provide alternative explanation for cause and effect relationships. This is to say, two components of analysis may vary in a correlation to each other without having any causal relationship to each other. One potential explanation for this is the "third variable confound." To be sure, it is sometimes the case that two characteristics of analysis may react in a similar manner to an influence but do not react in accordance of the intrinsic nature of the other. Instead, both of the characteristics share a causal relationship to a third characteristic. The confound exists when one of the original characteristics exists in the absence of the "third characteristic." We then expect to see the second "correlated" characteristic, but it is not present in the absence of the third "causal" characteristic.
Assumptive Biases and Artifacts:
An analyst inevitably brings assumptions and heuristics to their analysis. Even a "common sense bias" may taint the analysis. This is to say, that some things are taken for granted because they make sense. We may conclude that the influence or efficacy of a separatist movement is based on its popular support. This is not always the case. A separatist movement may serve an important purpose for or represent political ideology more favorable to other more power actors in the region who fund and support the movement. Membership does not always equal popular support. Many separate movement in Africa raid villages and kidnap children to man their growing armies of separatists. Over structuring analysis may produce results consistent with the original hypothesis, yet fail to truly capture a broad generalizable analysis of the intelligence target. Such results are artifacts. If the contextual structure of an analysis is too specific it runs the danger of creating such artifacts. To be sure, narrowing specific criteria to analyze a broad question such as "the role of NSA in Sub-Saharan Africa" will produce analysis that is effective in only very specific circumstances, and will not serve broad analytical purposes.
The Atypical Correlate
In many historical studies it has been the case that the most reliable correlates have demonstrated little apparent connection to the hypothesis being studied. Whether by accident, imagination, or diligent study, researchers have uncovered lateral correlates that often represent the most indicative and reliable information in their analysis. Sometimes this correlate has been found by identifying "third variable confounds." These "diamond in the rough" patterns are sometimes difficult to find, but open-minded diligent analysis may uncover such intelligence gems.
The Algorithm and the Danger of Too Much Information:
Malcolm Gladwell, in his book Blink, describes how people sometimes use too much information to make a decision for a particular circumstance. Gladwell describes how the Cook County Hospital attempted to deal with the problem of predicting heart attacks in patients complaining of chest pain. A 4 factor algorithm, developed by Lee Goldman, proved significantly more effective than a doctor’s (expert’s) diagnosis (opinion/assessment). Gladwell’s conclusion is that we sometimes use too much information to produce our assessments of particular situations. We all know that family histories of heart attacks greatly increase one’s risk of having a heart attack in one’s life, but does it predict whether someone will have a heart attack over the next 72 hours? The answer is no, a family history simply presents an increased risk, but does not help identify the cause of chest pain, at least not significantly. Chest pain can be the result of several different causations (confounds), and an individual is likely to have many bouts of chest pain over their life, especially in later life. Genetic predisposition increases the chance of heart attacks primarily in later life. Therefore, genetic predisposition is a poor indicator for any 72 hour period. However, electrocardiographic (ECG) evidence, presence of fluid in the lungs, unstable angina, and systolic blood pressure in excess of 100mm Hg are good indicators of a looming heart attack threatening to strike in the next 72 hours. This means that any nurse or even you or I can be quickly trained to speedily predict a looming heart attack and outperform the average expert (cardiologist). The director of the Cook County Hospital, Dr. Brandon Reilly, found that the experts (doctors) were considering indicators such as family history, diabetes, cholesterol, etc which are long-term risk factors for heart attacks, but shed little light on the next 72 hours, and produce assessments with lower accuracy than those exclusively considering the 4 factor algorithm. The individual using the 4 factor algorithm is “high speed” going directly for the most effective indicators, and “low drag” ignoring information that is cumbersome and complicates the immediate need.
The Structured Analysis Approach:
Distilling the vast amount of information down into the most indicative and reliable pieces (indicators) is of paramount concern when tackling a query as broad as "determining the role of non-state actors in Sub-Saharan Africa." Once key indicative aspects of a non-state actor are identified, a general algorithmic model can be created for analyzing non-state actors. Anomaly-centric analysis can then be conducted to identify potential flaws in the algorithmic model. Such analysis will identify non-state actors that don't fit the model and can help refine the model. Using such a structured approach can combat analyst biases and prevent the danger of too much information.
3D Analysis: 09112007
3-D Analysis:
3-D analysis refers to the utilization of several dimensions (methods) of analysis to identify key indications of the query target. The analytical model demonstrates the interaction of multiple method analysis (see figure).
[[image:file:C:%5CDOCUME%7E1%5CMARKBL%7E1%5CLOCALS%7E1%5CTemp%5Cmsohtml1%5C01%5Cclip_image003.jpg width="473" height="299"]]
NSA-1 refers to non-state actor 1. NSA-1, in the above diagram, is analyzed on the basis of four indicators and four role assessment perspectives. NSA-1 analysis would consist of 16 cubes demonstrating the interaction between each indicator and each role assessment. Each cube would have an indicator value, a role assessment value and an interaction value (i.e. I-1 value A=R-1 value B...see diagram). Indicator interactions are then assessed and compared to reveal patterns in NSA indicators and NSA roles. These patterns can then be used to generate a indicator/role relationships. These relationships would then be the source of building a predictive model for the role of non-state actors in Sub-Saharan Africa.
[[image:file:C:%5CDOCUME%7E1%5CMARKBL%7E1%5CLOCALS%7E1%5CTemp%5Cmsohtml1%5C01%5Cclip_image005.jpg width="193" height="216"]]
Notes: 091107
3 Dimensional Analysis presents an opportunity to produce automated analysis. Least Squares has produced some advanced data systems capable of analyzing the data we collect. If our group was able to create a data source, a simple data mining machine would be capable of gathering and organizing data in multiple configurations. A genetically program analysis system could compare the configured data to weigh against defined values. For instance if we were to claim that indicator A correlates to characteristic value B then perfect correlation would result in 1 (A/B= 1). A genetically programmed data analysis system can make thousands of calculations to identify patterns and correlations. With the enhancement of fuzzy logic, the data analysis system could analyze the degree of correlation (i.e. A/B= 0.865 is a better (more successful) correlate than A/C= 0.658). Genetic programming paired with fuzzy logic could quickly identify informational elements that share the most correlation to analysis. Perhaps the data systems described in the Least Squares publication “Visualizing the Political Landscape” could be programmed for our purposes. I will consult with the group to see if they would consider approaching Mr. Reynolds and see if he has any programs that may serve our purposes.
Concept of Analysis: 091207
Conceptual Model of Analysis:
Analysis of the Role of Non-State Actors in Sub-Saharan Africa
Executive summary:
Team Least Squares is tasked with determining the role of non-state actors (NSA) in Sub-Saharan Africa. Additional guidance by the decision maker (Bill Reynolds) is to use multiple analytical techniques and maintain detailed documentation of these techniques for further assessment. Team Least Squares’ analytical concept is devised to utilize and compare the products of analytical techniques using a “3-Dimensional model.” This model will allow analysts and the decision maker to easily and efficiently see the interaction of multiple techniques and key indicators. The result will be a structured comparison of each technique and key indicator as they have been applied to a target non-state actor.
Initial Collection:
The initial collection will consist of a board survey of Sub-Saharan countries and the NSAs that reside within its borders. The survey will have three main purposes:
1) Initial collection will survey the available information and assess its meaning/relevance in international affairs.
2) Analysts will attempt to identify indicators of the role of NSAs in Sub-Saharan Africa.
3) Analysts will determine the analytical methods that will be most effective at answering the intelligence requirement
Indicators:
Some indicators will be obvious and typical. It is known that China’s strategy to acquire access to extractive industry is to gain access for the least cost, regardless of other political considerations. Therefore, countries in which extractive resources are owned by a minimal amount of individuals/interests present the greatest opportunity for China; where as, countries with privatized ownership of extractive resources would present China with multiple owners of oil field, mines, etc and inevitably cost more money to acquire such access. State ownership/ Private ownership of extractive resources is an indicator of China’s interest and engagement of a country for purposes of gaining access to extractive resources. What about atypical indicators?
Perhaps the percentage of a population afflicted with malaria or HIV directly correlates to membership in a terrorist or separatist organization. Perhaps the common religion or ideology of a country directly correlates to the strength of tribal bounds. Such indicators can only be identified through careful study. This is one of the purposes of the initial collection/ informational survey.
Analytical Methods:
Initial collection/informational survey will allow the analysts to develop an understanding of the “informational terrain” with regards to the role of NSA in Sub-Saharan Africa. Analysts will use this understanding of the informational terrain to select analytical methods most effective for developing analysis to satisfy the intelligence requirements. Analytical methods may include but are not limited to:
· Analysis of Competing Hypotheses
· Demographic Analysis
· Geographic Analysis
· Social Network Analysis
· Regression Analysis
· Link Analysis
· Citation Analysis
· Multi-Attribute Analysis
3-Dimensional Analysis:
3-Deminsional analysis refers to the use of multiple analytical techniques and their comparison to indicators. Team Least Squares will select several analytical methods to assessment the role of NSA in Sub-Saharan Africa. Those techniques will be used to assess individual NSAs. In order to compare a NSA to other actors, in terms of “role”, the analysts will devise a value scale to determine the functional and influential roles NSAs play in geopolitics. Analysts will also select the most accurate and indicative indicators, develop a similar value scale and assign values to each NSA. The analysts can then observe the interaction between analytical methods and indicators to identify patterns and the relationship between indicators and the assessments.
[[image:file:C:%5CDOCUME%7E1%5CMARKBL%7E1%5CLOCALS%7E1%5CTemp%5Cmsohtml1%5C01%5Cclip_image007.jpg width="576" height="364"]]
NSA-1 refers to non-state actor 1 (see figure above). NSA-1, in the above diagram, is analyzed on the basis of four indicators and four role assessment perspectives. NSA-1 analysis would consist of 16 cubes demonstrating the interaction between each indicator and each role assessment. Each cube would have an indicator value, a role assessment value and an interaction value (i.e. I-1 value “A”=R-1 value “B”...see diagram below). Indicator interactions are then assessed and compared to reveal patterns in NSA indicators and NSA roles. These patterns can then be used to generate indicator/role relationships. These relationships would then be the source of building a predictive model for the role of non-state actors in Sub-Saharan Africa.
[[image:file:C:%5CDOCUME%7E1%5CMARKBL%7E1%5CLOCALS%7E1%5CTemp%5Cmsohtml1%5C01%5Cclip_image008.jpg width="157" height="176"]]
3-Dimensional analysis also determines which analytical methods and indicators develop the most accurate intelligence picture of the different types of non-state actors (i.e. NGOs, paramilitary groups, etc). The end result is a measurable comparison of analytical method and indicator accuracy, as well as analytical confidence (see example 3-D matrix below).
[[image:file:C:%5CDOCUME%7E1%5CMARKBL%7E1%5CLOCALS%7E1%5CTemp%5Cmsohtml1%5C01%5Cclip_image010.jpg width="576" height="343" align="center"]]
Notes: 091807
Originally I proposed to the group that we structure our original collection efforts to collecting specific data pertinent to the issues surrounding non-state actors in sub-Saharan Africa. I even proposed a state-centric approach. This is to say identify characteristics of Sub-Saharan states that influence or encourage the behavior, existence, or success of non-state actors and indicate their role in geopolitics. This was rejected in favor of a unrestricted collection plan. This is reflected in the concept model as “survey of the informational terrain.” The group has decided to divide the countries in the Sub-Sahara among the members of the group. I have come to agree with the wisdom of group. While, a more structured method would produce more reliable conclusions, my plan runs the danger of following a scientific method where it would not serve the purposes of our analysis. This is to say, if we make a prediction (hypothesis) without an understanding of the informational terrain, we run the risk of only disproving our prediction and providing no real analysis. To be sure, if we start with a premise that certain indicators will demonstrate causal or correlative relationships to the target query (role of NSAs) we run the risk of concluding that indicators don’t demonstrate a causal or correlative relationship to the target query. This conclusion would not answer the CIR. This is not to say, that the idea is not dead, but we should have some feel for whether such relationships exist before pursuing the search for them.
I have decided to divide my unrestricted collection into three main parts: 1) Survey the works already produced on the role of non-state actors in Sub-Saharan Africa. 2) Produce a database of characteristics (mostly demographical data) of the countries assigned to me using the CIA World Factbook as a source. 3) List and categorize the NSAs in my countries, and attempt to develop to feel for their role and the information pertinent to.
3D Mod-Graphic 092307
The original 3D Analysis graphic was confusing so I created the following “Qubert” graphic to help the understanding of the concept.
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The concept is truly simpler than it looks. Simply stated, by comparing each indicator with each method of analysis the analyst is able to identify patterns and correlations to determine the most efficient method of analysis. Furthermore, the design is conceptual in nature. It can be represented in more difficult for humans, but easier for computers to understand formats, such as Excel.
Correlation/Pattern analysis would be simple at this point. If high values of indicator one (I1), (such as % of population infected with HIV) are consistent with high value of “influential role” produced by all four analytical techniques (such as link analysis, financial analysis, citation analysis, and analysis of competing hypotheses) then I1 indicates that analysts in general will assess the target NSA to have a high value for “influential role.” Like wise if high values of role assessment 1 (R1) (such as link analysis), are consistent with multiple indicators (such, % of pop below poverty level, distribution of wealth, telecommunication assets etc) then it may be possible to develop an algorithm to predict the outcome of link analysis in terms of the target NSA. To be sure, poverty levels great than 30% matched with distribution of wealth lower than 10% and few telecommunications assets may indicate that tribal bounds are strong in the target country or NSA. Analysis across NSAs may develop reliable indicators and algorithms that produce effect analysis. Simple equations such as I1/R1= X can be used to produce comparable data. For instance: if I1= 30% and R1= 8 then I1/R1= .333/8=.0416. Values for multiple NSAs can be compared. If values for I1/R1 generally fall between .03 and .05 then the prediction can be made that I1 correlates to R1 regardless of the variation in the individual values for I1 and R1. Therefore if I1 for another NSA equaled 50% then we may predict that R1 would equal 12.019 (I1= .5 so .5/.0416= 12.019). Likewise if R1 for another NSA equaled 6 we may predict that I1 would equal 25% (R1= 6 so 6•.0416= .2496). Lets us say that, from a random sample, we developed a mean value of .0416 for I1/R1 with a standard deviation of .009 at a .05 significance level. We may then say if: NSA X’s I1= 25% then R1= 6 with a variation of R1= 4.94 to 7.669 (I1/.0416 +/- 1SD) within one standard deviation of the mean with a .05 error.
I believe such a method would answer both of the decision-maker’s CIRs: 1) Determine the role of non-state actors in Sub-Saharan Africa; 2) Use multiple analytical methods and keep notes (journals) for review after the assessment. Implied CIR: determine the efficacy and viability of different analytical methods. Furthermore, the scope of the CIR “the role of NSAs in Sub-Saharan Africa” is large and difficult to tackle. Therefore, by using a method that can identify indicators and test them for accuracy, the task becomes somewhat manageable.
Perhaps the best way to conceive the 3D analysis model is to consider it a multi-attribute/multiple target regression analysis. It not only allows the analyst to measure the interaction between different attributes (indicators) and analytical methods but also allows the analyst to determine the relationship between multiple targets and attribute/method interaction, ergo 3 dimensional analysis. Excel can easily be programmed to produce such data; furthermore we have access SPSS through the Mercyhurst Psychology Department. 3D analysis may seem daunting but given the scope of our CIR it is likely the most efficient process of fulfilling the requirement.
Notes: 092307
Issues:
Micro Credit (Small Loans):
These loans are used to help individuals “break the cycle of poverty” by purchasing land or entrepreneurial activities. Need to look into the affect of these. Reports seem optimistic about this type of credit; however, I believe these reports are likely skewed by the positive and stable regulatory environment which is the real source of success.
Notes on the 3D Model 092507
I spoke with Wheaton yesterday about the 3D model. He expressed a concern that it may be too exact for the data. His concern is that the available data may not be reliable enough, and too subjective for such a strenuous and exacting method. His point is well taken. Using statistical methods consistent with the standards of the scientific community would seemed to indicate that the data used is exact. This is not the case. As Wheaton pointed out, even the CIA World Factbook data is not precise and in many cases outdated. Moreover, the indicators are compared to analytical techniques; therefore, they would only indicate how an analyst would assess the query they are trying to answer and not necessarily reflect the reality of the situation.
We need to redesign the model to detect the “ghost in the machine.” A pattern is a pattern, is a pattern and must be explained. Moreover, patterns facilitate anomaly-centric analysis. Anomaly centric analysis often produces the meatiest analysis. This is to say, analysis of entities (NSAs) that don’t fit the pattern reveal the nuances of real-world dynamics. These nuances often temper analysis and allow the analyst to know when to weigh information differently, and penetrate the barrier of appearances. There are two main forces at play here. 1) The “Common Sense Bias” or the “that makes sense bias” (otherwise known as the O’Reilly Factor). 2) The anomaly and its indicators.
The “that makes sense bias” occurs when a conclusion appears to follow a logical course of reasoning but fails to account for the reality of the situation it is assessing, and goes unchallenged because of its apparently logical approach. This bias can take the familiar form of the “confirmation bias,” which occurs when the model precedes analysis. In this case, the analyst comes to a conclusion then builds a pseudo-logical explanation to confirm his/her original premise. This type of analysis is chronically prone to syllogistic errors, failure to recognize evidence to the contrary, the third variable confound, cultural-specific maxims (mirror imaging bias) etc. However, this bias can also take the form of a cognitive schema. These schemas present a more daunting task to recognize their effect. Generally, schemas are not dynamic, and do not react to a changing environment. The most dangerous schema (and most dangerous bias for that matter) of all is the paradigm. The paradigm is a complex universal schema that dictates the behaviors, excepted cause and effect relationships, thought processes, and so on of an entire population of people (normally of a profession, discipline, etc).
Anomalies and Indicators are signals and warnings that a model or paradigm does not fit the situation you are assessing. Albert Einstein launched a scientific revolution by identifying anomalies and indicators of the old paradigm of physics (Newtonian Physics). His work lead to new paradigms (quantum physics, etc) and still to this day scientists struggle to develop a new paradigm that can universally explain the known universe (unified theory). Anomaly-centric analysis allows the analyst to zero in on model spoilers, or confounds and often leads the analyst to the most important analytical questions and elements.
Applying these concepts to our current intelligence requirement we may conclude the following: An NGO that is dedicated to making the largest possible impact on the aids epidemic in Africa, will target the countries with the largest percentage of HIV infected population. However, this is unlikely the case. There is a plethora of geopolitical, security, and resource concerns that an NGO must deal with. If the original premise is not the case, why is it not the case? What are the common factors that are present in the countries where HIV focused NGOs concentrate their efforts. Likewise, we may conclude that: Countries, in which large percentages of their populations belong to many different tribes, will have a high degree of tribal conflicts. However, we may find that tribal in fighting correlates to the style of government or perhaps it correlates to the rate of HIV infection. If tribal in fighting correlates to the rate of HIV infection it is obvious that both correlate to a third unidentified variable, whether it be the supply of HIV medication, or even the supply of healthy fertile women, or simply the fact that the tribal balance of power has been offset by deaths due to aids. If we find that the tribal balance of power has been affected by HIV, we can, not only make assessments on the role of tribal organizations in the region but also determine the potential or actual role of an HIV focused NGO.
Anomaly-Centric Analysis 092507
Executive Summary:
Anomaly-Centric Analysis (ACA) is an abbreviated from of the scientific method of hypothesis testing. ACA begins by the analyst developing a simple pseudo-logical model of the expected influential factors of analysis. Next the analyst tests the model and searches for anomalies. The analyst targets anomalies and analyzes them to produce an advanced understanding of the cause and effect relationship between the influential factors of analysis. ACA is designed to identify the nuances and indicators that most provide insight into the reality of the situation or entity being assessed.
The Model:
Analysts generally cue into certain factors they believe to be at the heart of cause and effect relationships and or correlative elements (indicators) to produce predictive analysis. This often runs the risk of “confirmation bias.” Confirmation bias is present when a premise precedes analysis. Often the analyst will ignore evidence to the contrary of the premise, seek confirming evidence, utilized cultural-specific maxims (mirror-imaging bias), etc. However, the fact is that analysts cannot afford to take a “Humeian” approach and question all that they know. We as analysts must develop certain procedures and cognitive schemas to develop timely and accurate analysis. ACA is a structured analysis approach. To be sure, the analyst develops a model, collects specifically indicated information and uses standardized techniques to develop analysis in all situations.
ACA begins by the analyst making logical conclusions about the influential elements of his intelligence requirement (the query). The analyst then develops a model that he/she believes will develop accurate analysis. This process not only allows the analyst to develop structure for analysis, but it identifies the analyst’s assumptions about the intelligence requirement (IR). For example, an analyst may conclude that the more small tribes an African country has, the higher the rate of tribal conflict present in that country. Therefore, the analyst’s model would consist of the number of tribes, and the sizes of tribes, correlating to the rate of tribal conflict in the country. The analyst may create an equation as such:
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The analyst then begins to assess his model. Simply by producing a spreadsheet of the countries in the target region and their respective “# of tribes” and the “avg. percent of population belonging to these tribes” the analyst can then create a third column (inserting the equation Column A• Column B, or cell A1• B1) calculating the conflict coefficient. The analyst can then order the sheet by the conflict coefficient column and begin to test his/her model. At this point the analyst should develop method of determining the rate of conflict in the country. Using open source methods should provide the analyst ample information to determine how many armed conflicts, demonstrations, etc. a country has experienced for a specified time period. The analyst may simply list the numbers of conflict incidents or on going struggles that a country is experiencing or assign a scaled number value (e.g. 1-10). The analyst may start by comparing the countries containing the highest and lowest conflict coefficient. If the analyst’s model is accurate, there should be a relatively proportionate correlative variance between the conflict coefficient and actual rate of conflict between the countries. The analyst should proceed by comparing the entire list. If the analyst’s conflict coefficient and actual conflict rate does not correlate then he/she must look to other potential influential elements.
Perhaps the analyst determines that geographic location respective to the tribes is an influential factor in tribal conflict. The analyst may then add another variable to his/her equation. Perhaps the analyst determines that the average number of tribes bordering a single tribe’s territory is an influential factor. Perhaps he/she determines that the number of disputed territory is an influential factor, or for simplicity’s sake the analyst may determine that population density will suffice to address the issue. Therefore the analyst would augment the model and perhaps develop an equation as such:
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Again the analyst tests the conflict coefficient to see if it generally correlates to the actual rate of conflict in the target countries. The analyst should continue to develop the model adding columns to his/her spreadsheet, combining and add factors in various ways until he/she develops model that generally reflects the reality of the intelligence query. If you can divide the list of countries into 3 or 4 groups (depending on the size of the sample) and can say that groups generally fall into an accurate lower, middle, and upper 33, or 25 percentiles, respectively, then you have likely created a model that reflects the major influential factors of your query. Building an exact model is not likely possible. The analyst should not attempt to account for every nuance of the query. When the model generally reflects reality, the analyst is ready for anomaly centric analysis.
Before we continue, the question should be addressed whether an analyst is capable of producing such a model. Analysts produce assessments based on the information available. They use indicative information to determine unknown factors, such as analysis of future events (predictive analysis), analysis of enemy behavior and strategy (counter-intuitive analysis), trends, patterns, tactics, techniques, etc. The very production of analysis insinuates that the analyst is able to survey available information determines its meaning/relevance, determine its reliability, and determine what the information indicates. Failure to create a model that generally represents the influential factors of an intelligence query should bring the analyst pause. Of course individual situations require individual consideration; however, analyst who are using schemas and weighing information in a way that does not reflect the major influences relevant to their intelligence query should reassess their analysis. We began by developing a model based on the analysts assumptions about the intelligence requirement, if the analyst fails to produce a model that generally reflects reality then the analyst should examine his/her assumptions. In this way ACA helps to battle analytical biases and address the issues raise by modern research into the psychology of intelligence analysis.
Anomaly Centric Analysis:
Once a suitable model has been developed to represent the majority of influential concerns of a target query, the analyst should begin to target the anomalies. While the model is far from perfect, due to the fact that you will likely never develop a model that can respond to dynamism of the real world, the model does serve the purpose of identifying the entities that do not fit the model. This is where the analyst finds the nuances of individual situations. Any analyst will tell you that you must address each dimension of context (environmental, social, political, etc), specific to the intelligence requirement in order to create reliable analysis, anomaly centric analysis is where the analysts addresses these contextual elements. If a target-specific context exerts its influence it is likely to distort the target queries position in the model. For instance, perhaps a country does not fit the model. Country X has a few large tribes, which have historically secure borders. The majority of control in the government is determined by resources which reside well within the boundaries of tribal territory. The tribes have, for decades, welded relatively equivalent power and influence in the country. Conflicts have been restricted to a few border regions, and have not generally affected the government’s stability. However, over the past few years inter-tribal fighting has significantly increased and increasing deeper border incursion have been reported by one tribe. Assessments have increasingly indicated that stability in the region is deteriorating, and civil war seems all but a matter of time. You know that the country does not fit the model. You know that there is a factor beyond tribe number, size, and population density. You identify an anomaly and you target it for analysis. It does not matter what you find, perhaps an HIV epidemic has offset the balance of power between the tribes, perhaps there is a new Islamic or communist movement moving through the country. Your conclusions however, become an indicator. You may conclude that a regional rise in HIV is an indicator that tribal conflict will increase. The nuances identified in anomaly centric analysis can develop into a system of indicators and warnings, indicating trends and likely events. They may indicate the affects of an up coming election. An HIV epidemic may start in one country and grow. The identified nuance/indicator may grow to become a primary factor in the model. ACA can evolve over time to react to a dynamic environment. Any analyst knows that analysis is never done; it grows and changes with the times. Analysts can share, tweak, and discuss models and spreadsheets. Over time models can be perfected, and adapt to fit a changing world. ACA clearly presents the analyst’s assumptions for other analysts to evaluate, it is an interactive, competitive, and naturally select (based on success) process.
Conclusion:
Although the above example is purely theoretical, it demonstrates the adaptive, analytical nature of ACA. ACA develops and tests the analyst’s assumptions, identifies the influential anatomy of an intelligence query, helps the analyst target nuances, and identifies indicators and warnings. ACA is a process built to evolve. Someday you may use the Blair Model of Tribal Conflict, and later the Blair-Smith Model, which competes with the Johnson-Rodriquez Model. The information age has brought analysts closer than ever, using ACA thousands of models may compete for dominance, judged by their accuracy and flexibility. Information systems coded with fuzzy logic, and genetic programming may someday produce automated intelligence, identifying anomalies and bringing them to the attention of the analyst. ACA’s structured approach provides some common ground from which assessments may be analyzed and compared to other assessments. ACA develops a platform from which analysis is converted from ambiguous processes to clearly understandable process, freeing up the analyst to concentrate on the nuanced nature of intelligence queries.
Success!
We have done it! In a meeting with Wheaton today we were describing our new environmental, state-centric approach. In this approach the assumption is that role precedes the actor. The team came up with a new approach to measure how socio-political environments enhance and inhibit the roles of non-state actors (NSA). We talked about using an unfolding scale. A score of 0 would indicate total government control and no role for NSAs. From this point the role of NSA can move in two directions as the state governments demonstrate less control over the socio-political environment. First, a government can willing relinquish control over the activities of NSA, as well as guarantee and regulate them. This would include forms of popular representation, lobbyism, unionization, freedoms afforded by constitutions, the purchase and sale of mineral rights, privatization of economy etc. This type of willing relinquishment of power provides the potential for NSA to play and maintain influential roles. However, this dynamic would enhance the roles of lawful actors, such as labor unions, lawful corporations, activists, NGOs, etc. In contrast these types of regulated freedoms, or liberties and the institutions created to guarantee and enforce them would inhibit the role of unlawful actors such as insurgent groups, criminal organizations, corrupt corporations, etc. The second way a government can lose power is by its inability to regulate the behavior of NSAs. Unlawful NSAs use power sources that are beyond the regulation and sanction of the government. These “extra-governmental” power sources/behaviors include violence, intimidation, bribery, extortion, etc. Therefore, by our model, two states with the score of 4 in either direction of loss of government control have equivalent roles in terms of influence for NSAs, except in one state the role is filled by lawful actors, such as the corporations and political groups of the US, and in the other state the role is filled by unlawful actors, such as the Warlords of Somalia.
Wheaton upon hearing this drew a picture on the white board. He explained that what we had done, was to develop a spectrum by which we can represent NSA in terms of state control and represent the two different types of actors. An hour of conversation later, developing Wheaton’s drawing the model was born. We were able to identify the extremes and the middle of such a spectrum. On one extreme we have libertarian states, dead center we should find totalitarian states, and on the other extreme we should find anarchic states. Therefore, any score or ranking on an index or country data consistent with totalitarian states should represent the center of the model or a 0. We can now represent all types of environments which enhance the different types of NSAs. The group is now all on the same page. From now on I suspect my journal entries will be in our methodology descriptions.